How Do AI Companies "Fine-Tune" Policy? Examining Regulatory Capture in AI Governance

TL;DR

This paper investigates how AI industry actors influence US AI policy, potentially leading to regulatory capture that prioritizes private interests over public safety, fairness, and transparency, through expert interviews and social science models.

Contribution

It introduces models of regulatory capture in AI policy, presents empirical insights from expert interviews, and offers recommendations to mitigate industry influence in AI governance.

Findings

Industry influence channels include agenda-setting, advocacy, and media capture.

Experts are concerned about weak or biased AI regulation due to industry influence.

Proposed systemic reforms aim to reduce regulatory capture in AI policy.

Abstract

Industry actors in the United States have gained extensive influence in conversations about the regulation of general-purpose artificial intelligence (AI) systems. Although industry participation is an important part of the policy process, it can also cause regulatory capture, whereby industry co-opts regulatory regimes to prioritize private over public welfare. Capture of AI policy by AI developers and deployers could hinder such regulatory goals as ensuring the safety, fairness, beneficence, transparency, or innovation of general-purpose AI systems. In this paper, we first introduce different models of regulatory capture from the social science literature. We then present results from interviews with 17 AI policy experts on what policy outcomes could compose regulatory capture in US AI policy, which AI industry actors are influencing the policy process, and whether and how AI industry actors attempt to achieve outcomes of regulatory capture. Experts were primarily concerned with capture leading to a lack of AI regulation, weak regulation, or regulation that over-emphasizes certain policy goals over others. Experts most commonly identified agenda-setting (15 of 17 interviews), advocacy (13), academic capture (10), information management (9), cultural capture through status (7), and media capture (7) as channels for industry influence. To mitigate these particular forms of industry influence, we recommend systemic changes in developing technical expertise in government and civil society, independent funding streams for the AI ecosystem, increased transparency and ethics requirements, greater civil society access to policy, and various procedural safeguards.